source: Andrie de Vries

David Robinson summarized the goal on his laptop

Whenever you’re learning a new tool, for a long time you’re going to suck…
But the good news is that it's typical, that’s something that happens to everyone, and it’s only temporary.
– Hadley Wickham

vector, lists, data.frame, matrix, array etc

Lists are objects that could contain anything

list(a = 1:3, b = c("hello", "bye"), data = head(iris, 2))

## $a
## [1] 1 2 3
## 
## $b
## [1] "hello" "bye"  
## 
## $data
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa

Focus on data.frame which are lists where all columns have equal length

class(iris)

## [1] "data.frame"

class(unclass(iris))

## [1] "list"

Actually, better to use tbl_df

• Package management (also for building)
• Console to run R, with syntax highligther
• Editor to work with scripts / markdown
• autocompletion using TAB
• Cheatsheets
• Keyboard shortcuts

Cmd + Enter: sends line or selection from the editor to the console and runs it. (Ctrl + Enter on a PC) ↑: in the console browse previous commands

Recommended in the r4ds To get a clean environment at start-up

Solve most issues with working directory, get rid of setwd()

magrittr by Stefan Milton Bache

Compare

set.seed(124)
x <- rnorm(10)
mean(x)

## [1] 0.2147669

round(mean(x), 3)

## [1] 0.215

with

set.seed(124)
rnorm(10) %>% mean %>% round(3)

## [1] 0.215

• Variable: A quantity, quality, or property that you can measure.
• Observation: A set of values that display the relationship between variables.
• To be an observation, values need to be measured under similar conditions, usually measured on the same observational unit at the same time.
• Value: The state of a variable that you observe when you measure it.

source: Garret Grolemund

1. Each variable is in its own column
2. Each observation is in its own row
3. Each value is in its own cell

The wide format is generally untidy but found in the majority of datasets

tidyr convert from one to another

Wide > Long Long > Wide

head(iris, 3)

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa

gather

library("tidyr")
iris_melt <- iris %>%
  tibble::rownames_to_column() %>%
  dplyr::tbl_df() %>%
  gather(flower, measure, contains("al"))
iris_melt

## Source: local data frame [600 x 4]
## 
##    rowname Species       flower measure
##      <chr>  <fctr>        <chr>   <dbl>
## 1        1  setosa Sepal.Length     5.1
## 2        2  setosa Sepal.Length     4.9
## 3        3  setosa Sepal.Length     4.7
## 4        4  setosa Sepal.Length     4.6
## 5        5  setosa Sepal.Length     5.0
## 6        6  setosa Sepal.Length     5.4
## 7        7  setosa Sepal.Length     4.6
## 8        8  setosa Sepal.Length     5.0
## 9        9  setosa Sepal.Length     4.4
## 10      10  setosa Sepal.Length     4.9
## ..     ...     ...          ...     ...

spread

iris_melt %>%
  spread(flower, measure)

## Source: local data frame [150 x 6]
## 
##    rowname    Species Petal.Length Petal.Width Sepal.Length Sepal.Width
##      <chr>     <fctr>        <dbl>       <dbl>        <dbl>       <dbl>
## 1        1     setosa          1.4         0.2          5.1         3.5
## 2       10     setosa          1.5         0.1          4.9         3.1
## 3      100 versicolor          4.1         1.3          5.7         2.8
## 4      101  virginica          6.0         2.5          6.3         3.3
## 5      102  virginica          5.1         1.9          5.8         2.7
## 6      103  virginica          5.9         2.1          7.1         3.0
## 7      104  virginica          5.6         1.8          6.3         2.9
## 8      105  virginica          5.8         2.2          6.5         3.0
## 9      106  virginica          6.6         2.1          7.6         3.0
## 10     107  virginica          4.5         1.7          4.9         2.5
## ..     ...        ...          ...         ...          ...         ...

unite

df %>%
  unite(date, c(year, month, day), sep = "-") -> df_unite

separate, use quotes since not refering to objects

df_unite %>%
  separate(date, c("year", "month", "day"))

## Source: local data frame [3 x 4]
## 
##    year month   day value
##   <chr> <chr> <chr> <chr>
## 1  2015    11    23  high
## 2  2014     2     1   low
## 3  2014     4    30   low

Using Rstudio, right bottom panel. Select the folder

Using Rstudio, right top panel. Select directly your file

library("tidyr")
library("ggplot2")
iris %>%
  gather(flower, measure, 1:4) %>%
  ggplot()+
  geom_boxplot(aes(x = Species, y = measure, fill = flower))

iris %>%
  ggplot(aes(x = Sepal.Length, y = Sepal.Width, colour = Species))+
  geom_point()+
  geom_smooth(method = "lm", se = FALSE)+
  xlab("Length")+
  ylab("Width")+
  ggtitle("Sepal")

iris %>%
  ggplot(aes(x = Sepal.Length, y = Sepal.Width,
             size = Petal.Length / Petal.Width,
             colour = Species))+
  geom_point()+
  scale_size_area("Petal ratio Length / Width")+
  #scale_colour_brewer(palette = 1, type = "qual")+
  scale_colour_manual(values = c("blue", "red", "orange"))+
  xlab("Sepal.Length")+
  ylab("Sepal.Width")

iris %>%
  ggplot(aes(x = Sepal.Length, y = Sepal.Width))+
  geom_point(aes(colour = Species))

iris %>%
  ggplot(aes(x = Sepal.Length, y = Sepal.Width))+
  geom_point(aes(colour = "Species"))

iris %>%
  ggplot(aes(x = Sepal.Length, y = Sepal.Width))+
  geom_point(colour = "red")

iris_melt %>%
  ggplot()+
  geom_bar(aes(x = Species, y = measure, fill = flower), stat = "identity")

transparency using the alpha parameter

iris_melt %>%
  ggplot()+
  geom_density(aes(x = measure, fill = Species, colour = Species), alpha = 0.6)+
  facet_wrap(~ flower, scale = "free")+
  theme_bw()

transparency using the alpha parameter

iris_melt %>%
  ggplot()+
  geom_density(aes(x = measure, fill = Species, colour = Species), alpha = 0.6)+
  facet_grid(Species ~ flower, scale = "free")+
  theme_bw()

• R for data science by Hadley & Garrett
• reading data
• tidy data
• plotting
• ggplot2 layer by layer by Hadley
• Excellent ressource about R (in French) Ewen Gallic by Ewen Gallic

Hadley Wickham Garrett Grolemund Jenny Bryan Ewen Gallic Simon David Robinson